Optimization method of C2 system architecture based on ALCARO

To address the lack of combat mission-driven and guided optimization methods in existing command and control (C2) system architectures, this paper proposes an optimization method for C2 system architecture based on an improved artificial rabbit optimization (ARO) algorithm, specifically the adaptive inertia weight, Levy flight, and chaotic opposite-based learning in artificial rabbit optimization (ALCARO). This method introduces a collaborative connection degree for C2 system architectures, quantitatively describing the degree of collaboration between combat units and same-level C2 units, and establishes a mathematical model for the optimization problem of C2 system architecture. The ALCARO algorithm innovatively incorporates adaptive inertia weight, Levy flight, and piecewise chaotic mapping-based opposite learning strategies to enhance the algorithm's convergence speed, effectively avoiding premature convergence to local optima and demonstrating robust performance. Simulation results show that this method can effectively enhance the connectivity among collaborative combat units in a mission-oriented manner and possesses excellent invulnerability.